Improving Research through Avoiding Common Statistical Errors: The Case of Piosphere

Purpose: The importance of statistical analysis in medical research papers is ever increasing, hence, evaluation of statistical validity is crucial when evidence based medicine is highly valued. Studies with poor methodological quality and poor statistics cannot prove or disprove study hypothesis with certainty. This study was designed to evaluate, analyze and compare the reporting of statistical methods and errors in articles published in Indian Journal of Pharmacology (IJP) and Journal of Association of Physicians of India (JAPI). Materials and Methods: All original articles published in IJP and JAPI from January 2009 to September 2014 were reviewed and evaluated by using a checklist which included type of statistical test, common errors, etc. The statistical software used for analysis of data in these articles were also reviewed. Results: Three hundred articles (IJP=154; JAPI=146) were reviewed. The most commonly used statistical test in IJP was one-way ANOVA (53.8%) as compared to Chi-square test (50.6%) in JAPI. The statistical software used for analysis was mentioned in 43.5% and 50.7% articles published in IJP and JAPI respectively. The most commonly used software was GraphPad Prism (66.4%) in IJP and SPSS (67%) in JAPI. Statistical errors as per the checklist were more common in JAPI (63.5%) as against 49% in IJP. Use of mean+SE instead of Mean+SD was the most common statistical error in IJP (51.9%) whereas failure to mention the type of 't' test was the most common error (38%) in JAPI. Conclusion: Statistical errors are common in IJP as well as JAPI. To elevate the quality of articles published in Indian journals, every article must be sent for statistical review.

Statistical literacy has been emphasised in the school mathematics curriculum, with the growing concern about students’ ability to think critically in solving statistical problem-solving tasks. However, the current studies revealed that secondary school students’ errors mainly involve the problem of basic concepts in statistics, data interpretation, and the selection of an appropriate representation of data. Therefore, this study aimed to analyse the common errors made by students in solving statistics tasks with multi-level complexity. A survey method was applied in this study. The sample of this study consisted of 356 Form One (Grade 7) students from eight secondary schools. The instrument of this study consisted of five superitem tasks, which represented the five content domains: line graph, bar graph, pie chart, dot plot, and histogram. There are four levels of items in each superitem task. Thus, the total number of items is 20. The format of all the 20 items in the five superitem tasks is open-ended. The common errors were then analysed based on all the participants’ solutions shown in their answer script. The findings found that most students could not achieve the highest level of statistical competency. They failed to think qualitatively while justifying data. This study provides a meaningful analysis that assists the teaching and learning of statistics to better link numeracy and literacy. The application of the superitem tasks provides valuable information that enables the teachers to understand their students’ statistical processes better.

"Common Errors in Statistics (and How to Avoid Them)." Journal of Quality Technology, 37(1), pp. 87–88

(2004). Common Errors in Statistics (and How to Avoid Them) The American Statistician: Vol. 58, No. 4, pp. 359-359.

[1] Common Errors in Statistics (and How to Avoid Them), by Philip I. Good and James W. Hardin, 2006, John Wiley and Sons Inc., 111 River Street, MS 8-01, Hoboken, NJ 07030-5774, USA, ISBN: 0 471 7...

Manuscripts submitted to journals should be understandable even to those who are not experts in a particular field. Moreover, they should use publicly available materials and the results should be verifiable and reproducible. Readers and reviewers will want to check the strengths and weaknesses of the research study design, and ways to make this determination should be clear through proper analysis methods. Studies should be described in detail so as to help readers understand the results. Statistical analysis is one of the key methods by which to do this. The inappropriate application of statistical methods could be misleading to readers and clinicians. While many researchers describe their general research methods in detail, statistical methods tend to be described briefly, with certain omissions or errors or other incorrect aspects. For instance, researchers should describe whether the median or mean was used, whether parametric or nonparametric tests were used, whether the data meet the normality test, whether confounding factors were corrected, and whether stratification or matching methods were used. Statistical analysis regardless of the program should be reported correctly. The results may be less reliable if the statistical assumptions before applying the statistical method are not met. These common errors in statistical methods originate from the researcher's lack of knowledge of statistics and/or from the lack of any statistical consultation. The aim of this work is to help researchers know what is important statistically and how to present it in papers.

As statistical reviewers and editors for BMJ Paediatrics Open (BMJPO), we frequently see methodological and statistical errors in articles submitted to our journal. To make a list of these common...

Don't judge a book by its cover, don't judge a study by its abstract. Common statistical errors seen in medical papers.

<strong>Background: </strong>Statistical concepts and techniques are often applied incorrectly, even in mature disciplines such as medicine or psychology. Surprisingly, there are very few works that study statistical problems in software engineering (SE).<strong>Aim: </strong>Assess the existence of statistical errors in SE experiments.<strong>Method: </strong>Compile the most common statistical errors in experimental disciplines. Survey experiments published in ICSE to assess whether errors occur in high quality SE publications.<strong>Results:</strong> The same errors as identified in others disciplines were found in ICSE experiments, where 30% of the reviewed papers included several error types such as: a) missing statistical hypotheses, b) missing sample size calculation, c) failure to assess statistical test assumptions, and d) uncorrected multiple testing. This rather large error rate is greater for research papers where experiments are confined to the validation section. The origin of the errors can be traced back to: a) researchers not having sufficient statistical training, and, b) a profusion of exploratory research.<strong>Conclusions:</strong> This paper provides preliminary evidence that SE research suffers from the same statistical problems as other experimental disciplines. However, the SE community appears to be unaware of any shortcomings in its experiments, whereas other disciplines work hard to avoid these threats. Further research is necessary to find the underlying causes and set up corrective measures, but there are some potentially effective actions and are a priori easy to implement: a) improve the statistical training of SE researchers, and b) enforce quality assessment and reporting guidelines in SE publications.

Background: Statistical concepts and techniques are often applied incorrectly, even in mature disciplines such as medicine or psychology. Surprisingly, there are very few works that study statistical problems in software engineering (SE). Aim: Assess the existence of statistical errors in SE experiments. Method: Compile the most common statistical errors in experimental disciplines. Survey experiments published in ICSE to assess whether errors occur in high quality SE publications. Results: The same errors as identified in others disciplines were found in ICSE experiments, where 30% of the reviewed papers included several error types such as: a) missing statistical hypotheses, b) missing sample size calculation, c) failure to assess statistical test assumptions, and d) uncorrected multiple testing. This rather large error rate is greater for research papers where experiments are confined to the validation section. The origin of the errors can be traced back to: a) researchers not having sufficient statistical training, and, b) a profusion of exploratory research. Conclusions: This paper provides preliminary evidence that SE research suffers from the same statistical problems as other experimental disciplines. However, the SE community appears to be unaware of any shortcomings in its experiments, whereas other disciplines work hard to avoid these threats. Further research is necessary to find the underlying causes and set up corrective measures, but there are some potentially effective actions and are a priori easy to implement: a) improve the statistical training of SE researchers, and b) enforce quality assessment and reporting guidelines in SE publications.

Comprehensive Understanding of Gut Microbiota in Treatment Naïve Diffuse Large B Cell Lymphoma Patients

The first edition of this book was reviewed in this journal (Hayden, 2004) but not the second edition. This third edition sees the work expanded by about 50 pages, and the bibliography and indices ...

Phillip I. Good, James W. Hardin, Common errors in statistics

Book Review: Common errors in statistics (and how to avoid them)

Books reviewed in this article: Common Errors in Statistics (and How to Avoid Them). 2d Edition Philip I. Good and James W. Hardin Statistical thinking in busineess, 2nd Edition J Bobe E J A John, D Whitaker & D G Johnson Mathematical Statistics with Applications. A S Kapadia, W Chan and L Moyé The Cambridge Dictionary of Statistics. 3rd Edition. B. S. Everitt The Oxford Dictionary of Statistical Terms. 6th edition Y. Dodge